论文标题
轻巧的时间自我注意用于分类卫星图像时间序列
Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
论文作者
论文摘要
地球观察卫星数据的可及性和精度越来越高，为工业和州参与者提供了相当大的机会。但是，这呼吁有效的方法能够在全球范围内处理时间序列。在最新的工作的基础上，我们采用了多头自我发项机制来对遥感时间序列进行分类，我们提出了对时间关注编码器的修改。在我们的网络中，时间输入的通道分布在同行工作的几个紧凑型注意力头之间。每个头部都提取高度特征的时间特征，这些时间特征又被串联成单个表示。我们的方法优于开放式卫星图像数据集上其他最新的时间序列分类算法，同时使用明显较少的参数和降低的计算复杂性。
The increasing accessibility and precision of Earth observation satellite data offers considerable opportunities for industrial and state actors alike. This calls however for efficient methods able to process time-series on a global scale. Building on recent work employing multi-headed self-attention mechanisms to classify remote sensing time sequences, we propose a modification of the Temporal Attention Encoder. In our network, the channels of the temporal inputs are distributed among several compact attention heads operating in parallel. Each head extracts highly-specialized temporal features which are in turn concatenated into a single representation. Our approach outperforms other state-of-the-art time series classification algorithms on an open-access satellite image dataset, while using significantly fewer parameters and with a reduced computational complexity.
